An amperometric glucose biosensor based on a MnO2/graphene composite modified electrode

In this paper, a novel composite of graphene/MnO2 (GR/MnO2) was successfully synthesized by a simple one-step hydrothermal method. The as-synthesized MnO2 and the composite were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FTIR). The results showed that MnO2 was nanorods and the two materials were perfectly composited. The composite was decorated on a glassy carbon electrode (GCE) and used for the entrapment of glucose oxidase (GOD). Electrochemical results showed that the composite modified electrode showed a pair of well-defined redox peaks, and the direct electron transfer between GOD and the electrode surface was accelerated. The sensor fabricated by the composite modified electrode showed an excellent response to the oxidation of glucose with a wide linear range (0.04 to 2 mM), low detection limit (10 mM), and high sensitivity (3.3 mA mM-1 cm-2). The sensor also exhibited excellent reproducibility, stability and selectivity, and it can be used in the determination of glucose in real samples

Effect of Fans’ Placement on the Indoor Thermal Environment of Typical Tunnel-Ventilated Multi-Floor Pig Buildings Using Numerical Simulation

An increasing number of large pig farms are being built in multi-floor pig buildings (MFPBs) in China. Currently, the ventilation system of MFPB varies greatly and lacks common standards. This work aims to compare the ventilation performance of three popular MFPB types with different placement of fans using the Computational Fluid Dynamics (CFD) technique. After being validated with field-measured data, the CFD models were extended to simulate the air velocity, air temperature, humidity, and effective temperature of the three MFPBs. The simulation results showed that the ventilation rate of the building with outflowing openings in the endwall and fans installed on the top of the shaft was approximately 25% less than the two buildings with fans installed on each floor. The ventilation rate of each floor increased from the first to the top floor for both buildings with a shaft, while no significant difference was observed in the building without a shaft. Increasing the shaft’s width could mitigate the variation in the ventilation rate of each floor. The effective temperature distribution at the animal level was consistent with the air velocity distribution. Therefore, in terms of the indoor environmental condition, the fans were recommended to be installed separately on each floor

Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning

Visual aesthetic assessment has been an active research field for decades. Although latest methods have achieved promising performance on benchmark datasets, they typically rely on a large number of manual annotations including both aesthetic labels and related image attributes. In this paper, we revisit the problem of image aesthetic assessment from the self-supervised feature learning perspective. Our motivation is that a suitable feature representation for image aesthetic assessment should be able to distinguish different expert-designed image manipulations, which have close relationships with negative aesthetic effects. To this end, we design two novel pretext tasks to identify the types and parameters of editing operations applied to synthetic instances. The features from our pretext tasks are then adapted for a one-layer linear classifier to evaluate the performance in terms of binary aesthetic classification. We conduct extensive quantitative experiments on three benchmark datasets and demonstrate that our approach can faithfully extract aesthetics-aware features and outperform alternative pretext schemes. Moreover, we achieve comparable results to state-of-the-art supervised methods that use 10 million labels from ImageNet.Comment: AAAI Conference on Artificial Intelligence, 2020, accepte

NEOLAF, an LLM-powered neural-symbolic cognitive architecture

This paper presents the Never Ending Open Learning Adaptive Framework (NEOLAF), an integrated neural-symbolic cognitive architecture that models and constructs intelligent agents. The NEOLAF framework is a superior approach to constructing intelligent agents than both the pure connectionist and pure symbolic approaches due to its explainability, incremental learning, efficiency, collaborative and distributed learning, human-in-the-loop enablement, and self-improvement. The paper further presents a compelling experiment where a NEOLAF agent, built as a problem-solving agent, is fed with complex math problems from the open-source MATH dataset. The results demonstrate NEOLAF's superior learning capability and its potential to revolutionize the field of cognitive architectures and self-improving adaptive instructional systems

Comparison of immature and mature bone marrow-derived dendritic cells by atomic force microscopy

A comparative study of immature and mature bone marrow-derived dendritic cells (BMDCs) was first performed through an atomic force microscope (AFM) to clarify differences of their nanostructure and adhesion force. AFM images revealed that the immature BMDCs treated by granulocyte macrophage-colony stimulating factor plus IL-4 mainly appeared round with smooth surface, whereas the mature BMDCs induced by lipopolysaccharide displayed an irregular shape with numerous pseudopodia or lamellapodia and ruffles on the cell membrane besides becoming larger, flatter, and longer. AFM quantitative analysis further showed that the surface roughness of the mature BMDCs greatly increased and that the adhesion force of them was fourfold more than that of the immature BMDCs. The nano-features of the mature BMDCs were supported by a high level of IL-12 produced from the mature BMDCs and high expression of MHC-II on the surface of them. These findings provide a new insight into the nanostructure of the immature and mature BMDCs

The relation between amyotrophic lateral sclerosis and inorganic selenium in drinking water: a population-based case-control study

<p>Abstract</p> <p>Background</p> <p>A community in northern Italy was previously reported to have an excess incidence of amyotrophic lateral sclerosis among residents exposed to high levels of inorganic selenium in their drinking water.</p> <p>Methods</p> <p>To assess the extent to which such association persisted in the decade following its initial observation, we conducted a population-based case-control study encompassing forty-one newly-diagnosed cases of amyotrophic lateral sclerosis and eighty-two age- and sex-matched controls. We measured long-term intake of inorganic selenium along with other potentially neurotoxic trace elements.</p> <p>Results</p> <p>We found that consumption of drinking water containing ≥ 1 μg/l of inorganic selenium was associated with a relative risk for amyotrophic lateral sclerosis of 5.4 (95% confidence interval 1.1-26) after adjustment for confounding factors. Greater amounts of cumulative inorganic selenium intake were associated with progressively increasing effects, with a relative risk of 2.1 (95% confidence interval 0.5-9.1) for intermediate levels of cumulative intake and 6.4 (95% confidence interval 1.3-31) for high intake.</p> <p>Conclusion</p> <p>Based on these results, coupled with other epidemiologic data and with findings from animal studies that show specific toxicity of the trace element on motor neurons, we hypothesize that dietary intake of inorganic selenium through drinking water increases the risk for amyotrophic lateral sclerosis.</p

Effect of ikaite precipitation on phosphate removal in sea ice

Ikaite (CaCO3·6H2O) precipitation in sea ice has been shown to affect CO2&nbsp;exchange between the atmosphere and ocean. A laboratory study indicates that it could also co-precipitate phosphate from sea ice, which has the potential to affect sea-ice biogeochemical processes. However, the relative importance of ikaite precipitation on phosphate removal under sea-ice conditions remains unknown. We investigated ikaite precipitation in both frost flowers and seaice (under two scenarios: flooded by seawater and non-flooded) in an outdoor sea-ice mesocosm experiment, and in sea ice under natural conditions in north-eastern Greenland. The ice mesocosm experiment showed that ikaite was highly enriched in frost flowers with a concentration of up to 350 µmol·kg–1. Ikaite was also detected in the surface layer of sea ice, ranging from ca. 13 µmol·kg–1&nbsp;in the non-flooded ice to ca. 95 µmol·kg–1&nbsp;in the flooded ice. However, under all these conditions, no phosphate co-precipitation with ikaite was observed. The field study in Greenland showed similar results: ikaite was detected in surface ice with an average concentration of 13.8 µmol·kg–1, but no phosphate removal due to ikaite precipitation was observed. These results suggest that the impact of ikaite precipitation on phosphate and the sea-ice ecosystem might not be as significant as imagined previously

Evaluation and Optimization of the Oil Jet Lubrication Performance for Orthogonal Face Gear Drive: Modelling, Simulation and Experimental Validation

The oil jet lubrication performance of a high-speed and heavy-load gear drive is significantly influenced and determined by the oil jet nozzle layout, as there is extremely limited meshing clearance for the impinging oil stream and an inevitable blocking effect by the rotating gears. A novel mathematical model for calculating the impingement depth of lubrication oil jetting on an orthogonal face gear surface has been developed based on meshing face gear theory and the oil jet lubrication process, and this model contains comprehensive design parameters for the jet nozzle layout and face gear pair. Computational fluid dynamic (CFD) numerical simulations for the oil jet lubrication of an orthogonal face gear pair under different nozzle layout parameters show that a greater mathematically calculated jet impingement depth results in a greater oil volume fraction and oil pressure distribution. The influences of the jet nozzle layout parameters on the lubrication performance have been analyzed and optimized. The relationship between the measured tooth surface temperature from the experiments and the corresponding calculated impingement depth shows that a lower temperature appears in a situation with a greater impingement depth. Good agreement between the mathematical model with the numerical simulation and the experiment validates the effectiveness and accuracy of the method for evaluating the face gear oil jet lubrication performance when using the impingement depth mathematical model